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Record W7011525397

Measuring moderations: a cross cultural and comparative research in services between brazilians and canadians

2014· article· en· W7011525397 on OpenAlexaboutno aff

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsnot available
Fundersnot available
KeywordsModerationComparative researchTest (biology)Power (physics)Construct (python library)Order (exchange)Field (mathematics)Cross-culturalEmpirical research
DOInot available

Abstract

fetched live from OpenAlex

This study examines the relationship of relational benefits and their consequent variables by moderating cultural idiosyncrasies. The field research was conducted among 297 Brazilians and 207 Canadians. The collected data were analyzed by statistical tests such as comparing means, MANOVA, canonical correlation and moderation in regressive models in order to test the proposed technique. There were also methodological contributions through the development of computational scripts that identified the power and direction of each construct and path. The comparative analyses confirm that Brazilians are possibly more demanding than Canadians when evaluate services. On the one hand, due to Brazilians are culturally with greater power distance, we may indicate that to give them special treatment is an important factor in increasing satisfaction with employees. On the other hand, due to Canadians belong to a more egalitarian society, there are feelings that these privileges and "jeitinhos" should not be just for a few. The results obtained in this study may also be useful in strengthening business ties between Brazil and Canada in order to raise awareness among both countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.320
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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